TY - JOUR
T1 - Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients
AU - Joint Head and Neck MRI-Radiotherapy Development Cooperative
AU - Kiser, Kendall
AU - Meheissen, Mohamed A.M.
AU - Mohamed, Abdallah S.R.
AU - Kamal, Mona
AU - Ng, Sweet Ping
AU - Elhalawani, Hesham
AU - Jethanandani, Amit
AU - He, Renjie
AU - Ding, Yao
AU - Rostom, Yousri
AU - Hegazy, Neamat
AU - Bahig, Houda
AU - Garden, Adam
AU - Lai, Stephen
AU - Phan, Jack
AU - Gunn, Gary B.
AU - Rosenthal, David
AU - Frank, Steven
AU - Brock, Kristy K.
AU - Wang, Jihong
AU - Fuller, Clifton D.
N1 - Publisher Copyright:
© 2019
PY - 2019/9
Y1 - 2019/9
N2 - Background: MRI-guided radiotherapy planning (MRIgRT) may be superior to CT-guided planning in some instances owing to its improved soft tissue contrast. However, MR images do not communicate tissue electron density information necessary for dose calculation and therefore must either be co-registered to CT or algorithmically converted to synthetic CT. No robust quality assessment of commercially available MR-CT registration algorithms is yet available; thus we sought to quantify MR-CT registration formally. Methods: Head and neck non-contrast CT and T2 MRI scans acquired with standard treatment immobilization techniques were prospectively acquired from 15 patients. Per scan, 35 anatomic regions of interest (ROIs) were manually segmented. MRIs were registered to CT rigidly (RIR) and by three commercially available deformable registration algorithms (DIR). Dice similarity coefficient (DSC), Hausdorff distance mean (HD mean) and Hausdorff distance max (HD max) metrics were calculated to assess concordance between MRI and CT segmentations. Each DIR algorithm was compared to DIR using the nonparametric Steel test with control for individual ROIs (n = 105 tests) and for all ROIs in aggregate (n = 3 tests). The influence of tissue type on registration fidelity was assessed using nonparametric Wilcoxon pairwise tests between ROIs grouped by tissue type (n = 12 tests). Bonferroni corrections were applied for multiple comparisons. Results: No DIR algorithm improved the segmentation quality over RIR for any ROI nor all ROIs in aggregate (all p values >0.05). Muscle and gland ROIs were significantly more concordant than vessel and bone, but DIR remained non-different from RIR. Conclusions: For MR-CT co-registration, our results question the utility and applicability of commercially available DIR over RIR alone. The poor overall performance also questions the feasibility of translating tissue electron density information to MRI by CT registration, rather than addressing this need with synthetic CT generation or bulk-density assignment.
AB - Background: MRI-guided radiotherapy planning (MRIgRT) may be superior to CT-guided planning in some instances owing to its improved soft tissue contrast. However, MR images do not communicate tissue electron density information necessary for dose calculation and therefore must either be co-registered to CT or algorithmically converted to synthetic CT. No robust quality assessment of commercially available MR-CT registration algorithms is yet available; thus we sought to quantify MR-CT registration formally. Methods: Head and neck non-contrast CT and T2 MRI scans acquired with standard treatment immobilization techniques were prospectively acquired from 15 patients. Per scan, 35 anatomic regions of interest (ROIs) were manually segmented. MRIs were registered to CT rigidly (RIR) and by three commercially available deformable registration algorithms (DIR). Dice similarity coefficient (DSC), Hausdorff distance mean (HD mean) and Hausdorff distance max (HD max) metrics were calculated to assess concordance between MRI and CT segmentations. Each DIR algorithm was compared to DIR using the nonparametric Steel test with control for individual ROIs (n = 105 tests) and for all ROIs in aggregate (n = 3 tests). The influence of tissue type on registration fidelity was assessed using nonparametric Wilcoxon pairwise tests between ROIs grouped by tissue type (n = 12 tests). Bonferroni corrections were applied for multiple comparisons. Results: No DIR algorithm improved the segmentation quality over RIR for any ROI nor all ROIs in aggregate (all p values >0.05). Muscle and gland ROIs were significantly more concordant than vessel and bone, but DIR remained non-different from RIR. Conclusions: For MR-CT co-registration, our results question the utility and applicability of commercially available DIR over RIR alone. The poor overall performance also questions the feasibility of translating tissue electron density information to MRI by CT registration, rather than addressing this need with synthetic CT generation or bulk-density assignment.
KW - CT-MRI image registration
KW - Deformable image registration
KW - MRI-guided radiotherapy
KW - Quality assessment
KW - Rigid image registration
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U2 - 10.1016/j.ctro.2019.04.018
DO - 10.1016/j.ctro.2019.04.018
M3 - Article
C2 - 31341987
AN - SCOPUS:85071350295
SN - 2405-6308
VL - 18
SP - 120
EP - 127
JO - Clinical and Translational Radiation Oncology
JF - Clinical and Translational Radiation Oncology
ER -